




"""simple docstring"""




import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


lowerCAmelCase				:     Optional[int]					=				"""sshleifer/mar_enro_6_3_student"""






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							super().setUp()

							lowerCamelCase      =   cached_path(
							    """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""						,					extract_compressed_file=_a						,					)
							lowerCamelCase      =   f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'





				@slow
				@require_torch_gpu
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							MarianMTModel.from_pretrained(_a					)





				@slow
				@require_torch_gpu
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   {
							    """$MAX_LEN""": 64,
							    """$BS""": 64,
							    """$GAS""": 1,
							    """$ENRO_DIR""": self.data_dir,
							    """facebook/mbart-large-cc25""": MARIAN_MODEL,
							    # "val_check_interval=0.25": "val_check_interval=1.0",
							    """--learning_rate=3e-5""": """--learning_rate 3e-4""",
							    """--num_train_epochs 6""": """--num_train_epochs 1""",
							}

							# Clean up bash script
							lowerCamelCase      =   (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""					)[1].strip()
							lowerCamelCase      =   bash_script.replace("""\\\n"""						,					""""""					).strip().replace("""\"$@\""""						,					""""""					)
							for k, v in env_vars_to_replace.items():
										lowerCamelCase      =   bash_script.replace(_a						,					str(_a					)					)
							lowerCamelCase      =   self.get_auto_remove_tmp_dir()

							# bash_script = bash_script.replace("--fp16 ", "")
							lowerCamelCase      =   f'\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        '.split()
							# XXX: args.gpus > 1 : handle multi_gpu in the future

							lowerCamelCase      =   ["""finetune.py"""] + bash_script.split() + args
							with patch.object(_a						,					"""argv"""						,					_a					):
										lowerCamelCase      =   argparse.ArgumentParser()
										lowerCamelCase      =   pl.Trainer.add_argparse_args(_a					)
										lowerCamelCase      =   SummarizationModule.add_model_specific_args(_a						,					os.getcwd()					)
										lowerCamelCase      =   parser.parse_args()
										lowerCamelCase      =   main(_a					)

							# Check metrics
							lowerCamelCase      =   load_json(model.metrics_save_path					)
							lowerCamelCase      =   metrics["""val"""][0]
							lowerCamelCase      =   metrics["""val"""][-1]
							self.assertEqual(len(metrics["""val"""]					)						,					(args.max_epochs / args.val_check_interval)					)
							assert isinstance(last_step_stats[f'val_avg_{model.val_metric}']						,					_a					)

							self.assertGreater(last_step_stats["""val_avg_gen_time"""]						,					0.01					)
							# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
							self.assertLessEqual(last_step_stats["""val_avg_gen_time"""]						,					1.0					)

							# test learning requirements:

							# 1. BLEU improves over the course of training by more than 2 pts
							self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""]						,					2					)

							# 2. BLEU finishes above 17
							self.assertGreater(last_step_stats["""val_avg_bleu"""]						,					17					)

							# 3. test BLEU and val BLEU within ~1.1 pt.
							self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""]					)						,					1.1					)

							# check lightning ckpt can be loaded and has a reasonable statedict
							lowerCamelCase      =   os.listdir(_a					)
							lowerCamelCase      =   [x for x in contents if x.endswith(""".ckpt"""					)][0]
							lowerCamelCase      =   os.path.join(args.output_dir						,					_a					)
							lowerCamelCase      =   torch.load(_a						,					map_location="""cpu"""					)
							lowerCamelCase      =   """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
							assert expected_key in ckpt["state_dict"]
							assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

							# TODO: turn on args.do_predict when PL bug fixed.
							if args.do_predict:
										lowerCamelCase      =   {os.path.basename(_a					) for p in contents}
										assert "test_generations.txt" in contents
										assert "test_results.txt" in contents
										# assert len(metrics["val"]) ==  desired_n_evals
										assert len(metrics["""test"""]					) == 1






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''





				@timeout_decorator.timeout(600					)
				@slow
				@require_torch_gpu
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   f'{self.test_file_dir_str}/test_data/wmt_en_ro'
							lowerCamelCase      =   {
							    """--fp16_opt_level=O1""": """""",
							    """$MAX_LEN""": 128,
							    """$BS""": 16,
							    """$GAS""": 1,
							    """$ENRO_DIR""": data_dir,
							    """$m""": """sshleifer/student_marian_en_ro_6_1""",
							    """val_check_interval=0.25""": """val_check_interval=1.0""",
							}

							# Clean up bash script
							lowerCamelCase      =   (
							    (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""					)[1].strip()
							)
							lowerCamelCase      =   bash_script.replace("""\\\n"""						,					""""""					).strip().replace("""\"$@\""""						,					""""""					)
							lowerCamelCase      =   bash_script.replace("""--fp16 """						,					""" """					)

							for k, v in env_vars_to_replace.items():
										lowerCamelCase      =   bash_script.replace(_a						,					str(_a					)					)
							lowerCamelCase      =   self.get_auto_remove_tmp_dir()
							lowerCamelCase      =   bash_script.replace("""--fp16"""						,					""""""					)
							lowerCamelCase      =   6
							lowerCamelCase      =   (
							    ["""distillation.py"""]
							    + bash_script.split()
							    + [
							        f'--output_dir={output_dir}',
							        """--gpus=1""",
							        """--learning_rate=1e-3""",
							        f'--num_train_epochs={epochs}',
							        """--warmup_steps=10""",
							        """--val_check_interval=1.0""",
							        """--do_predict""",
							    ]
							)
							with patch.object(_a						,					"""argv"""						,					_a					):
										lowerCamelCase      =   argparse.ArgumentParser()
										lowerCamelCase      =   pl.Trainer.add_argparse_args(_a					)
										lowerCamelCase      =   SummarizationDistiller.add_model_specific_args(_a						,					os.getcwd()					)
										lowerCamelCase      =   parser.parse_args()
										# assert args.gpus == gpus THIS BREAKS for multi_gpu

										lowerCamelCase      =   distill_main(_a					)

							# Check metrics
							lowerCamelCase      =   load_json(model.metrics_save_path					)
							lowerCamelCase      =   metrics["""val"""][0]
							lowerCamelCase      =   metrics["""val"""][-1]
							assert len(metrics["""val"""]					) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

							assert last_step_stats["val_avg_gen_time"] >= 0.01

							assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
							assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
							assert isinstance(last_step_stats[f'val_avg_{model.val_metric}']						,					_a					)

							# check lightning ckpt can be loaded and has a reasonable statedict
							lowerCamelCase      =   os.listdir(_a					)
							lowerCamelCase      =   [x for x in contents if x.endswith(""".ckpt"""					)][0]
							lowerCamelCase      =   os.path.join(args.output_dir						,					_a					)
							lowerCamelCase      =   torch.load(_a						,					map_location="""cpu"""					)
							lowerCamelCase      =   """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
							assert expected_key in ckpt["state_dict"]
							assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

							# TODO: turn on args.do_predict when PL bug fixed.
							if args.do_predict:
										lowerCamelCase      =   {os.path.basename(_a					) for p in contents}
										assert "test_generations.txt" in contents
										assert "test_results.txt" in contents
										# assert len(metrics["val"]) ==  desired_n_evals
										assert len(metrics["""test"""]					) == 1









"""simple docstring"""




from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map



def        a__					(							snake_case__							)		->   Dict[str, torch.Tensor]:
			lowerCamelCase      =   []
			lowerCamelCase      =   []
			lowerCamelCase      =   []

			for rt in rc.restypes:
						lowerCamelCase      =   rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
						restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]							)
						lowerCamelCase      =   {name: i for i, name in enumerate(snake_case__							)}
						restype_atomaa_to_atomaa_list.append(
						    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]							)

						restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]							)

			# Add dummy mapping for restype 'UNK'
			restype_atomaa_to_atomaa_list.append([0] * 14							)
			restype_atomaa_to_atomaa_list.append([0] * 37							)
			restype_atomaa_mask_list.append([0.0] * 14							)

			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.intaa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   torch.tensor(
			    snake_case__			,					dtype=torch.floataa			,					device=protein["""aatype"""].device			,					)
			lowerCamelCase      =   protein["""aatype"""].to(torch.long							)

			# create the mapping for (residx, atom14) --> atom37, i.e. an array
			# with shape (num_res, 14) containing the atom37 indices for this protein
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]

			lowerCamelCase      =   residx_atomaa_mask
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the gather indices for mapping back
			lowerCamelCase      =   restype_atomaa_to_atomaa[protein_aatype]
			lowerCamelCase      =   residx_atomaa_to_atomaa.long()

			# create the corresponding mask
			lowerCamelCase      =   torch.zeros([21, 37]			,					dtype=torch.floataa			,					device=protein["""aatype"""].device							)
			for restype, restype_letter in enumerate(rc.restypes							):
						lowerCamelCase      =   rc.restype_atoa[restype_letter]
						lowerCamelCase      =   rc.residue_atoms[restype_name]
						for atom_name in atom_names:
									lowerCamelCase      =   rc.atom_order[atom_name]
									lowerCamelCase      =   1

			lowerCamelCase      =   restype_atomaa_mask[protein_aatype]
			lowerCamelCase      =   residx_atomaa_mask

			return protein



def        a__					(							snake_case__							)		->   Dict[str, np.ndarray]:
			lowerCamelCase      =   tree_map(lambda snake_case__							: torch.tensor(snake_case__			,					device=batch["""aatype"""].device							)			,					snake_case__			,					np.ndarray							)
			lowerCamelCase      =   tensor_tree_map(lambda snake_case__							: np.array(snake_case__							)			,					make_atomaa_masks(snake_case__							)							)
			return out



